Online scheduling for FPGA computation in the Cloud

Dai, Guohao; Shan, Yi; Chen, Fei; Wang, Yu; Wang, Kun; Yang, Huazhong

doi:10.1109/fpt.2014.7082811

Cited by 18 publications

(7 citation statements)

References 6 publications

(11 reference statements)

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“…After introducing FPGAs as cloud resources, many implementations of these algorithms are being proposed in the literature, making FPGA scheduling an active research topic. For example, the proposed scheduling algorithm in [19] is based on the virtualization framework of Chen et al in [4]. Accelerator slots (PRRs) of various sizes are obtained because of the virtualization and the resources that the proposed scheduler provisions.…”

Section: Fpga Scheduling Approaches In Cloudmentioning

confidence: 99%

Virtualizing and Scheduling FPGA Resources in Cloud Computing Datacenters

et al. 2022

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Cloud service providers consistently leverage their computing infrastructures by adding reconfigurable hardware platforms such as field-programmable gate arrays (FPGAs) to their existing infrastructures. Adding FPGAs to a cloud environment involves non-trivial challenges. The first challenge is virtualizing FPGAs as part of the cloud resources. As a standard virtualization framework is lacking, there is a need for an efficient framework for virtualizing FPGAs. Furthermore, FPGA resources are used in conjunction with central processing units (CPUs) and graphics processing units (GPUs) to accelerate the execution of tasks. Therefore, to gain the benefits of these powerful accelerating platforms, the second challenge is to optimize the allocation of tasks into the capable resources within a cloud data center. This work proposes an FPGA virtualization framework that abstracts the physical FPGAs into virtual pools of FPGA resources. The work further presents an integer linear programming (ILP) optimization model to minimize the makespan of tasks where FPGA resources are part of the cloud data center. Given the complex nature of the problem, a simulated annealing (SA) metaheuristic is developed to achieve gains in performance compared to the exact method and to scale up and handle many tasks and resources while providing nearoptimal solutions. Experimental results show that SA has reduced the makespan of a large dataset with 1000 tasks and 100 resources by up to 30% when compared to first-come-first-served (FCFS) and shortestdeadline-first (SDF) algorithms. Lastly, to quantify the performance of FPGA-enabled cloud datacenters, the work extends the CloudSim simulator (an open-source cloud simulator) to enable FPGA as a resource in its environment. The proposed virtualization framework and the SA scheduler are integrated into the environment. Simulation results show that the execution time of tasks is reduced by up to 78% when FPGA accelerators are used.

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Section: Fpga Scheduling Approaches In Cloudmentioning

confidence: 99%

Virtualizing and Scheduling FPGA Resources in Cloud Computing Datacenters

et al. 2022

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“…However, these two algorithms are designed to reduce the overall energy consumption, resulting in lower task parallelism. The authors of [16] proposed a benefit-based scheduling metric to evaluate the task assignment. However, they did not fully consider the resource contention between different tasks.…”

Section: Related Workmentioning

confidence: 99%

Resource-Aware Task Scheduling and Placement in Multi-FPGA System

Sun

Zhang

2019

IEEE Access

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With the development of high performance computing, Field Programmable Gate Arrays (FPGAs) are widely used for task acceleration. Partial reconfigurable technology provides the possibility of multi-task concurrent computation on FPGAs. However, it is still difficult to achieve high performance multi-task acceleration in multi-FPGA systems due to the resource contention in limited hardware resources and reconfiguration overhead. In this paper, we propose a two-stage task scheduling approach in multi-FPGA systems to optimize task execution efficiency. At the first scheduling stage, we select an appropriate computing unit for each task considering the subtask similarity and resource requirement similarity to reduce the possibility of reconfiguration and resource contention. At the second scheduling stage, we coordinate the subtask scheduling and placement to make full use of the hardware resources of FPGAs and improve the acceleration performance. Experiments show that our two-stage scheduling approach significantly reduces the tasks makespan and improves the utilization of resources compared with other traditional methods. INDEX TERMS Multi-FPGA, task scheduling, task placement, partial reconfiguration.

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“…Chen et al propose a general framework for integrating FPGAs into the cloud based on a priority‐based workload scheduling. Guohao et al design an online benefit‐based scheduling algorithm for FPGAs in cloud environments. Beach et al propose the use of a broker‐based matchmaking system to select the most suitable accelerator device for a particular application kernel.…”

Section: Related Workmentioning

confidence: 99%

Power and performance optimization in FPGA‐accelerated clouds

Tesfatsion

Proaño

Tomás

et al. 2018

Concurrency and Computation

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Energy management has become increasingly necessary in data centers to address all energy-related costs, including capital costs, operating expenses, and environmental impacts.Heterogeneous systems with mixed hardware architectures provide both throughput and processing efficiency for different specialized application types and thus have a potential for significant energy savings. However, the presence of multiple and different processing elements increases the complexity of resource assignment. In this paper, we propose a system for efficient resource management in heterogeneous clouds. The proposed approach maps applications' requirement to different resources reducing power usage with minimum impact on performance. A technique that combines the scheduling of custom hardware accelerators, in our case, Field-Programmable Gate Arrays (FPGAs) and optimized resource allocation technique for commodity servers, is proposed. We consider an energy-aware scheduling technique that uses both the applications' performance and their deadlines to control the assignment of FPGAs to applications that would consume the most energy. Once the scheduler has performed the mapping between a VM and an FPGA, an optimizer handles the remaining VMs in the server, using vertical scaling and CPU frequency adaptation to reduce energy consumption while maintaining the required performance. Our evaluation using interactive and data-intensive applications compare the effectiveness of the proposed solution in energy savings as well as maintaining applications performance, obtaining up to a 32% improvement in the performance-energy ratio on a mix of multimedia and e-commerce applications.

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Online scheduling for FPGA computation in the Cloud

Cited by 18 publications

References 6 publications

Virtualizing and Scheduling FPGA Resources in Cloud Computing Datacenters

Virtualizing and Scheduling FPGA Resources in Cloud Computing Datacenters

Resource-Aware Task Scheduling and Placement in Multi-FPGA System

Power and performance optimization in FPGA‐accelerated clouds

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